Influence of graphene functionalization on the curing kinetics, dynamical mechanical properties and morphology of epoxy nanocomposites
Bibliographic record
Abstract
The properties of graphene have made it a promising material for the development of polymer nanocomposites, and graphene functionalization has gained popularity due to its ability to improve dispersion between the phases. For thermosetting matrices, nanomaterials can affect curing, and rheological studies provide crucial information about this process. This study was undertaken to investigate the impact of graphene functionalization on the curing kinetics and morphology of epoxy nanocomposites. For that, graphene (G), graphene functionalized with surfactant sodium dodecyl sulfate (G-SDS), graphene oxide (GO), and graphene oxide functionalized with amine groups (GON) were used as nanofillers. Rheological studies showed that the addition of graphene to the resin resulted in a slower curing reaction in comparison to the neat epoxy at temperatures of 60 and 70 °C. G-SDS did not affect the curing kinetics of the epoxy resin, while the addition of GO and GON to the resin accelerated the curing kinetics and reduced the reaction activation energy. The most significant improvements were observed for GON, with a reduction in gelation time at 60 °C from approximately 40 min to 17 min, and at 80 °C from 11 min to 6 min, compared to the neat epoxy. The functionalization also resulted in a significant increase in the dynamic storage (E′) and loss (E″) moduli, indicating that functionalization of graphene enhances its interfacial interaction with the epoxy matrix. Specifically, GON yielded a 70 % increase in E′ and a 28 % increase in E″ compared to the neat epoxy. • GO and GON significantly accelerated epoxy curing reactions. • Amine functionalization improved interaction with epoxy rings. • The longer chain length of DETA likely contributed to an increased reaction rate. • E′ increased by 70 % for GON/E compared to pure epoxy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".